CN113361831B - Non-invasive load identification electric quantity decomposition method and system based on priority distribution - Google Patents
Non-invasive load identification electric quantity decomposition method and system based on priority distribution Download PDFInfo
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Abstract
The non-invasive load identification electric quantity decomposition method and system based on priority distribution comprise load start and stop edge detection, load start and stop edge classification, first priority load electric quantity decomposition and second priority load electric quantity decomposition; according to the high-frequency sampling information collected and calculated by the non-invasive load identification equipment, detecting the start and stop edges of the load by adopting a maximum likelihood ratio algorithm, and acquiring the characteristic information of active power, reactive power and harmonic waves at the edges; then, calculating Euclidean distance of edge characteristic data, and classifying load edges so as to realize load type identification; finally, performing coulombic decomposition according to the two-step priority. The invention executes different electric quantity decomposition strategies based on the priority sequence, solves the problem of inaccurate electric quantity identification of power fluctuation characteristics of the air conditioner and the like, and can realize accurate electric quantity decomposition of a plurality of electric appliances in a simultaneous working state.
Description
Technical Field
The invention relates to the technical field of non-intrusive load identification, in particular to a non-intrusive load identification electric quantity decomposition method and system based on priority distribution.
Background
For a long time, the information sensing capability of the equivalent equipment of the intelligent electric energy meter at the user side is weak, accurate fine-grained load data and analysis service thereof cannot be provided, and the bidirectional information interaction between an electric power company and residential users is difficult to support.
The non-invasive load identification technology is a new technology for monitoring the electricity consumption state of residents in real time, and the high-frequency sampling data of equipment such as a distributed terminal and an intelligent electric energy meter are utilized, and signal analysis and a machine learning algorithm are applied, so that the electricity consumption information of the residents, such as the start-stop time, the energy consumption level, the use rule and the like of an electric appliance, can be acquired more finely. The technology can realize the accurate identification of the composition and energy consumption of residential electric equipment in all time periods, thereby providing value-added services such as energy-saving diagnosis, identification of potential safety hazards of electricity consumption, service life prediction of electric appliances, utilization rate statistics of electric appliances and the like for customers.
Under the background, the non-invasive load identification technology is widely concerned by researchers at home and abroad, and is always in high research heat. In the prior art, a non-invasive load identification process includes data acquisition, data preprocessing, event detection, feature extraction, load identification and other steps. The data preprocessing comprises the substeps of abnormal value elimination, power value calculation and the like, and the load identification comprises the substeps of load identification, power decomposition and the like. The research literature and patents of the load identification technology direction are numerous, and the Chinese patent application (CN 112039059A) 'a long transient load event detection method based on power step continuity judgment', the Chinese patent application (CN 111191908A) 'a load event detection method, a system and a storage medium' and the Chinese patent application (CN 104483575A) 'a self-adaptive load event detection method for non-invasive power monitoring' mainly concentrate on load event detection; the Chinese patent application (CN 112327046A) is a non-invasive load monitoring method based on fuzzy clustering and support vector regression, the Chinese patent application (CN 111949707A) is a non-invasive load decomposition method based on a hidden Markov model of an image field, the Chinese patent application (CN 111830347A) is a two-stage non-invasive load monitoring method based on events, and the Chinese patent application (CN 111553444A) is a load identification method based on non-invasive load terminal data, and the load type identification is concerned; in the terminal device and system direction, the representative technology is chinese utility model patent (CN 210781189U) "hardware device for collecting user electric meter load identification data", chinese utility model patent (CN 208297577U) "a user load identification module" capable of being embedded in an electric meter ", and chinese utility model patent (CN 204992779U)" a non-invasive electric load identification system ". However, there are few studies on non-invasive load electricity decomposition, and in the prior art, the chinese patent application (CN 111753968A) "non-invasive load monitoring intelligent ammeter and electricity decomposition method" adopts a sequence-to-point electricity decomposition model with an attention mechanism added, and performs fine total electricity decomposition by self-learning weights, whereas the chinese patent application (CN 109840691A) constructs a itemized electricity decomposition estimation method based on a deep neural network.
Whether the deep neural network model or the sequence-to-point electricity decomposition model is adopted, a large amount of training sample data required by the model is difficult to obtain in practical application. The difference of the types of the electric appliances among different families is huge, the situations of updating iteration and the like of the electric appliances of the same type cause the quantity of training samples to be increased sharply, otherwise, the identification accuracy is reduced. Meanwhile, the model needs to go through a long training and learning process, is time-consuming and is not suitable for a localized load identification device for real-time acquisition and real-time processing and analysis. Therefore, it is desirable to provide a non-intrusive load identification power decomposition method for distributed devices.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a non-invasive load identification electric quantity decomposition method and a non-invasive load identification electric quantity decomposition system based on priority distribution, which are used for performing decomposition calculation on the electric quantity of a load to be identified based on a multi-priority distribution method on the premise of completing quick detection and accurate matching of a load edge, so that the electric quantity decomposition precision in the non-invasive load identification technology is improved, and the influence of fluctuating load on electric quantity decomposition is avoided.
The invention adopts the following technical scheme.
The non-intrusive load identification electric quantity decomposition method based on priority distribution comprises the following steps:
step 1, carrying out high-frequency sampling on current and voltage of an in-house bus, and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period;
step 2, detecting a load edge by using the active power of a power frequency cycle and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of a direct current component and the increment of a harmonic current component of the load edge;
step 3, extracting characteristic vectors from the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge; identifying the type of the load edge by using the characteristic vector and each electric appliance characteristic vector in the electric appliance characteristic library through an Euclidean distance judgment method;
step 4, according to different types of load edges, performing multi-priority distribution on various loads to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load;
step 5, for any load edge, matching the load shutdown edge with the load startup edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
Preferably, in step 1, the current and voltage of the household bus are sampled at high frequency by using an electric energy meter or other terminal equipment, and the high-frequency sampled current and voltage are output by the metering core; wherein, the sampling frequency of the high-frequency sampling period is 1.6 kHz.
The step 1 comprises the following steps:
step 1.1, collecting current and voltage of the household bus based on a high-frequency sampling period, and respectively forming a current sampling sequence by utilizing high-frequency sampling values of the current and the voltage
And a voltage sampling sequence
；
Step 1.2, sampling sequence of current according to the following relation
And a voltage sampling sequence
Performing fast Fourier transform to obtain current amplitude result sequence
Current phase result sequence
Voltage amplitude result sequence
And a sequence of voltage phase results
：
In the formula (I), the compound is shown in the specification,
is a fast fourier transform of a sequence of current samples,
fast fourier transform of a sequence of voltage samples;
step 1.3, calculating the active power, the reactive power, the direct current component and the second component of the power frequency period according to the following relational expressionskSubharmonic current component:
in the formula (I), the compound is shown in the specification,kin order to be the harmonic frequency, the frequency of the harmonic wave,Pis the active power of the power frequency period,QIs the reactive power of the power frequency period,
Is a direct current of a power frequency period,
is the first of power frequency cyclekThe effective value of the sub-harmonic current,
is as followskThe magnitude of the current at the sub-harmonic,
is as followskThe voltage amplitude of the sub-harmonics,
is as followskThe phase of the current of the sub-harmonic,
is as followskThe phase of the voltage of the sub-harmonic,
is the current amplitude of the fundamental wave.
Preferably, step 2 comprises:
step 2.1, arranging active power, reactive power, direct current component and harmonic current component of a power frequency period into an active power sequence according to a period time sequence
Reactive power sequence
Sequence of direct current components
And a firstkSequence of sub-harmonic current components
；
Step 2.2, for the mth period, constructing a sliding data window for the active power sequence; wherein the sliding data window comprises a front sliding data window
And a rear sliding data window
(ii) a Wherein, the front sliding data window is an active power sequence formed by 50 active powers before the m period, and the front sliding data window is formed by 50 active powers after the m periodA resulting active power sequence;
step 2.3, calculating the maximum likelihood ratio of the sliding data window at the mth period according to the following relational expression
：
In the formula (I), the compound is shown in the specification,
is a function of the probability of the normal distribution,
for the mean value of the post-sliding data window,
for the post-sliding data window standard deviation,
is the mean of the front sliding window of data,
a front sliding data window standard deviation;
step 2.4, if the maximum likelihood ratio of the sliding data window at the mth period is larger than the set event threshold, judging that a load edge event exists at the mth period
(ii) a Wherein the set event threshold value is 0.5;
step 2.5, for the presence of load edge events
Period m of (a), if
Then the load edge event is judged to be a load starting edge event
(ii) a On the contrary, if
Then the load edge event is determined to be a load shutdown edge event
；
Step 2.6, respectively calculating the active power increment of the load edge occurring at the mth period according to the following relational expression
Reactive power increment
Delta of DC current component
And a firstkIncrement of subharmonic current component
：
In the formula (I), the compound is shown in the specification,
active power for the x-th cycle,
Is the x-th period reactive power,
Is the x-th cycle direct currentThe components of the first and second images are,
is the x periodkThe sub-harmonic current effective value.
Preferably, in step 3, the feature vector is a set of 9-dimensional feature vectors, including an active power increment, a reactive power increment, an increment of a direct current component, and an increment of a 2-7 th harmonic current component of a load edge occurring at the mth cycle; the electrical appliance feature library comprises active power increment, reactive power increment, increment of direct current component and increment of 2-7 harmonic current component of each electrical appliance.
The step 3 comprises the following steps:
step 3.1, traversing and calculating all Euclidean distances between the characteristic vectors and all the electric appliance characteristic vectors in the electric appliance characteristic library, and solving a maximum Euclidean distance value in the characteristic vectors;
step 3.2, if the obtained maximum Euclidean distance value is larger than a set distance threshold value, taking the type of the electric appliance in the electric appliance feature library corresponding to the maximum Euclidean distance value as the type of the load edge corresponding to the feature vector; wherein the set distance threshold value is 200.
Preferably, in step 4, the different types of the load edges are different types of the electric appliances in the electric appliance feature library, and the types of the electric appliances comprise electric appliances with stable working states and electric appliances with fluctuating working states; the electric appliances with stable working states are classified into a first priority load, and the electric appliances with fluctuating working states are classified into a second priority load.
Preferably, step 5 comprises:
step 5.1, for any load edge, shutting down the load to edge event
With a load start edge event occurring before the time of the load stop edge event
Matching to obtain load identification pairs
(ii) a Wherein the load stops the edge event
Take place inmAt cycle, load initiated edge events
Take place innAt period of time, satisfy
And the load edges corresponding to the two events are of the same kind;
step 5.2, identify the pair in any load
Next, the first priority load is decomposed to obtain the consumed power of the corresponding electrical appliance according to the following relation:
in the formula (I), the compound is shown in the specification,
step 5.3, identify the pair in any load
Next, integrating the total power of the second priority load from the starting time to the ending time of the second priority load to obtain the total electric quantity;
and 5.4, subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
The non-intrusive load identification electric quantity decomposition system based on priority distribution comprises: the system comprises a high-frequency sampling module, a load edge detection module, a load edge identification module, a load priority distribution module and a load identification electric quantity decomposition module;
the high-frequency sampling module is used for carrying out high-frequency sampling on the current and the voltage of the household bus and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period;
the load edge detection module is used for detecting the load edge by utilizing the active power of the power frequency cycle and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge;
the load edge identification module is used for extracting a characteristic vector from an active power increment, a reactive power increment, an increment of a direct current component and an increment of a harmonic current component of a load edge; identifying the type of the load edge by using the characteristic vector and each electric appliance characteristic vector in the electric appliance characteristic library through an Euclidean distance judgment method;
the load priority distribution module is used for carrying out multi-priority distribution on various loads according to different types of load edges so as to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load;
the load identification electric quantity decomposition module is used for matching a load shutdown edge with a load start edge for any load edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
Compared with the prior art, the load identification electric quantity decomposition strategy based on the multi-priority distribution method has the advantages that under the condition that the load edge is rapidly detected and accurately matched, non-air-conditioning electric appliances such as an electric water heater and an electric hair drier are brought into the first priority load, the electric quantity decomposition is carried out by adopting a trapezoidal method, and the electric quantity calculation is accurate and reliable by adopting the trapezoidal method due to the stable working state of the electric appliances; and aiming at the air-conditioning electric appliance, a second priority load is included, and the electric quantity is estimated by subtracting the stable non-air-conditioning electric quantity from the total electric quantity, so that the problem of inaccurate electric quantity decomposition caused by fluctuation of the working state of the air-conditioning electric appliance is effectively solved.
In addition, the method provided by the invention is easy to operate and realize in the methods of data acquisition, data preprocessing, event detection, feature extraction, load identification and the like, so that the method is more suitable for the localized load identification equipment with real-time acquisition and real-time processing analysis.
Drawings
FIG. 1 is a block flow diagram of a non-intrusive load identification power decomposition method based on priority assignment according to the present invention;
fig. 2 is a schematic structural diagram of a non-intrusive load identification electric quantity decomposition system based on priority assignment according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Referring to fig. 1, a method for non-intrusive load identification power decomposition based on multi-priority distribution includes:
step 1, carrying out high-frequency sampling on current and voltage of an in-house bus, and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period.
In particular, the amount of the solvent to be used,
in the step 1, an electric energy meter or other terminal equipment is used for carrying out high-frequency sampling on current and voltage of a home bus, and a metering core outputs high-frequency sampling current and voltage; wherein, the sampling frequency of the high-frequency sampling period is 1.6 kHz.
It should be noted that, those skilled in the art can calibrate the optimal high-frequency sampling frequency according to the requirements of multiple experiments or data acquisition, and in the preferred embodiment, 1.6kHz is used as the high-frequency sampling frequency, which is a non-limiting preferred choice.
Further, step 1 comprises:
step 1.1, collecting current and voltage of the household bus based on a high-frequency sampling period, and respectively forming a current sampling sequence by utilizing high-frequency sampling values of the current and the voltage
And a voltage sampling sequence
。
Step 1.2, sampling sequence of current according to the following relation
And a voltage sampling sequence
Performing fast Fourier transform to obtain current amplitude result sequence
Current phase result sequence
Voltage amplitude result sequence
And a sequence of voltage phase results
：
In the formula (I), the compound is shown in the specification,
is a fast fourier transform of a sequence of current samples,
is a fast fourier transform of a sequence of voltage samples.
Step 1.3, calculating the active power, the reactive power, the direct current component and the second component of the power frequency period according to the following relational expressionskSubharmonic current component:
in the formula (I), the compound is shown in the specification,kin order to be the harmonic frequency, the frequency of the harmonic wave,Pis the active power of the power frequency period,QIs the reactive power of the power frequency period,
Is a direct current of a power frequency period,
is the first of power frequency cyclekThe effective value of the sub-harmonic current,
is as followskThe magnitude of the current at the sub-harmonic,
is as followskThe voltage amplitude of the sub-harmonics,
is as followskThe phase of the current of the sub-harmonic,
is as followskThe phase of the voltage of the sub-harmonic,
is the current amplitude of the fundamental wave.
And 2, detecting the load edge by using the power frequency cycle active power and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge.
Specifically, step 2 comprises:
step 2.1, arranging active power, reactive power, direct current component and harmonic current component of a power frequency period into an active power sequence according to a period time sequence
Reactive power sequence
Sequence of direct current components
And a firstkSequence of sub-harmonic current components
。
Step 2.2, for the mth period, constructing a sliding data window for the active power sequence; wherein the sliding data window comprises a front sliding data window
And a rear sliding data window
(ii) a The front sliding data window is an active power sequence formed by 50 active powers before the mth period, and the front sliding data window is an active power sequence formed by 50 active powers after the mth period.
It should be noted that, a person skilled in the art may calibrate the number of data in the active power sequence according to multiple experiments or data acquisition requirements, and the active power sequence composed of 50 active powers in the preferred embodiment is a non-limiting preferred choice.
Step 2.3, calculating the maximum likelihood ratio of the sliding data window at the mth period according to the following relational expression
：
In the formula (I), the compound is shown in the specification,
is a function of the probability of the normal distribution,
for the mean value of the post-sliding data window,
for the post-sliding data window standard deviation,
is the mean of the front sliding window of data,
is the standard deviation of the front sliding data window.
Step 2.4, if the maximum likelihood ratio of the sliding data window at the mth period is larger than the set event threshold, judging that a load edge event exists at the mth period
(ii) a Wherein the set event threshold value is 0.5.
It should be noted that, in the preferred embodiment, the value of the event threshold is 0.5, which is a non-limiting preferred choice.
Step 2.5, for the presence of load edge events
Period m of (a), if
Then the load edge event is judged to be a load starting edge event
(ii) a On the contrary, if
Then the load edge event is determined to be a load shutdown edge event
。
Step 2.6, respectively calculating the active power increment of the load edge occurring at the mth period according to the following relational expression
Reactive power increment
D.c. currentIncrement of component
And a firstkIncrement of subharmonic current component
：
In the formula (I), the compound is shown in the specification,
active power for the x-th cycle,
Is the x-th period reactive power,
Is the x-th period direct current component,
is the x periodkThe sub-harmonic current effective value.
Step 3, extracting characteristic vectors from the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge; and identifying the type of the load edge by using the characteristic vector and the characteristic vectors of the electric appliances in the electric appliance characteristic library through an Euclidean distance judgment method.
Specifically, in step 3, the feature vector is a set of 9-dimensional feature vectors, including an active power increment, a reactive power increment, an increment of a direct current component, and an increment of a 2-7 th harmonic current component of a load edge occurring at the m-th cycle.
The electrical appliance feature library comprises active power increment, reactive power increment, increment of direct current component and increment of 2-7 harmonic current component of each electrical appliance.
Further, step 3 comprises:
and 3.1, traversing and calculating all Euclidean distances between the characteristic vectors and all the electric appliance characteristic vectors in the electric appliance characteristic library, and solving a maximum Euclidean distance value.
Step 3.2, if the obtained maximum Euclidean distance value is larger than a set distance threshold value, taking the type of the electric appliance in the electric appliance feature library corresponding to the maximum Euclidean distance value as the type of the load edge corresponding to the feature vector; wherein the set distance threshold value is 200.
It should be noted that, in the preferred embodiment, the distance threshold is 200, which is a non-limiting preferred choice.
Step 4, according to different types of load edges, performing multi-priority distribution on various loads to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load.
Specifically, in step 4, the different types of the load edges are different types of the respective appliances in the appliance feature library, and the types of the appliances include appliances with stable working states and appliances with fluctuating working states.
The electric appliances with stable working states are classified into a first priority load, and the electric appliances with fluctuating working states are classified into a second priority load.
In the preferred embodiment of the present invention, the first priority load includes but is not limited to non-air-conditioning appliances such as electric water heaters and hair dryers, and the second priority load is an air conditioner.
Step 5, for any load edge, matching the load shutdown edge with the load startup edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
Further, step 5 comprises:
step 5.1, for any loadEdge, load off edge event
With a load start edge event occurring before the time of the load stop edge event
Matching to obtain load identification pairs
(ii) a Wherein the load stops the edge event
Take place inmAt cycle, load initiated edge events
Take place innAt period of time, satisfy
And the load edges for both events are of the same kind.
Step 5.2, identify the pair in any load
Then, the power consumption of the corresponding electrical appliance is obtained by decomposing the power of the first priority load by using a trapezoidal method according to the following relation:
in the formula (I), the compound is shown in the specification,
step 5.3, identify the pair in any load
And integrating the total power of the second priority load from the starting time to the ending time of the second priority load to obtain the total electric quantity.
And 5.4, subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
As shown in fig. 2, the non-intrusive load identification electric quantity decomposition system based on priority distribution includes: the system comprises a high-frequency sampling module, a load edge detection module, a load edge identification module, a load priority distribution module and a load identification electric quantity decomposition module.
The high-frequency sampling module is used for carrying out high-frequency sampling on the current and the voltage of the household bus and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period;
the load edge detection module is used for detecting the load edge by utilizing the active power of the power frequency cycle and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge;
the load edge identification module is used for extracting a characteristic vector from an active power increment, a reactive power increment, an increment of a direct current component and an increment of a harmonic current component of a load edge; identifying the type of the load edge by using the characteristic vector and each electric appliance characteristic vector in the electric appliance characteristic library through an Euclidean distance judgment method;
the load priority distribution module is used for carrying out multi-priority distribution on various loads according to different types of load edges so as to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load;
the load identification electric quantity decomposition module is used for matching a load shutdown edge with a load start edge for any load edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
Compared with the prior art, the load identification electric quantity decomposition strategy based on the multi-priority distribution method has the advantages that under the condition that the load edge is rapidly detected and accurately matched, non-air-conditioning electric appliances such as an electric water heater and an electric hair drier are brought into the first priority load, the electric quantity decomposition is carried out by adopting a trapezoidal method, and the electric quantity calculation is accurate and reliable by adopting the trapezoidal method due to the stable working state of the electric appliances; and aiming at the air-conditioning electric appliance, a second priority load is included, and the electric quantity is estimated by subtracting the stable non-air-conditioning electric quantity from the total electric quantity, so that the problem of inaccurate electric quantity decomposition caused by fluctuation of the working state of the air-conditioning electric appliance is effectively solved.
In addition, the method provided by the invention is easy to operate and realize in the methods of data acquisition, data preprocessing, event detection, feature extraction, load identification and the like, so that the method is more suitable for the localized load identification equipment with real-time acquisition and real-time processing analysis.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (9)
1. The non-intrusive load identification electric quantity decomposition method based on priority distribution is characterized in that,
the decomposition method comprises the following steps:
step 1, carrying out high-frequency sampling on current and voltage of an in-house bus, and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period;
step 2, detecting a load edge by using the active power of a power frequency cycle and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of a direct current component and the increment of a harmonic current component of the load edge;
step 3, extracting characteristic vectors from the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge; identifying the type of the load edge by using the characteristic vector and each electric appliance characteristic vector in the electric appliance characteristic library through an Euclidean distance judgment method;
step 4, according to different types of load edges, performing multi-priority distribution on various loads to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load;
step 5, for any load edge, matching the load shutdown edge with the load startup edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
2. The method of non-intrusive load recognition battery power decomposition based on priority assignment as defined in claim 1,
in the step 1, an electric energy meter or other terminal equipment is used for carrying out high-frequency sampling on current and voltage of a home bus, and a metering core outputs high-frequency sampling current and voltage; wherein, the sampling frequency of the high-frequency sampling period is 1.6 kHz.
3. The method of non-intrusive load recognition battery decomposition based on priority assignment as defined in claim 2,
the step 1 comprises the following steps:
step 1.1, collecting current and voltage of the household bus based on a high-frequency sampling period, and respectively forming a current sampling sequence by utilizing high-frequency sampling values of the current and the voltage
And a voltage sampling sequence
；
Step 1.2, sampling sequence of current according to the following relation
And a voltage sampling sequence
Performing fast Fourier transform to obtain current amplitude result sequence
Current phase result sequence
Voltage amplitude result sequence
And a sequence of voltage phase results
：
In the formula (I), the compound is shown in the specification,
is a fast fourier transform of a sequence of current samples,
fast fourier transform of a sequence of voltage samples;
step 1.3, calculating the active power, the reactive power, the direct current component and the second component of the power frequency period according to the following relational expressionskSubharmonic current component:
in the formula (I), the compound is shown in the specification,kin order to be the harmonic frequency, the frequency of the harmonic wave,Pis the active power of the power frequency period,QIs the reactive power of the power frequency period,
Is a direct current of a power frequency period,
is the first of power frequency cyclekSubharmonic electricityThe effective value of the stream is,
is as followskThe magnitude of the current at the sub-harmonic,
is as followskThe voltage amplitude of the sub-harmonics,
is as followskThe phase of the current of the sub-harmonic,
is as followskThe phase of the voltage of the sub-harmonic,
is the current amplitude of the fundamental wave.
4. The method of non-intrusive load recognition battery power decomposition based on priority assignment as defined in claim 1,
the step 2 comprises the following steps:
step 2.1, arranging active power, reactive power, direct current component and harmonic current component of a power frequency period into an active power sequence according to a period time sequence
Reactive power sequence
Sequence of direct current components
And a firstkSequence of sub-harmonic current components
；
Step 2.2, forAt m periods, constructing a sliding data window for the active power sequence; wherein the sliding data window comprises a front sliding data window
And a rear sliding data window
(ii) a The front sliding data window is an active power sequence formed by 50 active powers before the mth period, and the front sliding data window is an active power sequence formed by 50 active powers after the mth period;
step 2.3, calculating the maximum likelihood ratio of the sliding data window at the mth period according to the following relational expression
：
In the formula (I), the compound is shown in the specification,
is a function of the probability of the normal distribution,
for the mean value of the post-sliding data window,
for the post-sliding data window standard deviation,
is the mean of the front sliding window of data,
is the standard deviation of the front sliding data window;
step 2.4, if the sliding data window at the m-th periodIs greater than the set event threshold value, the load edge event is judged to exist at the mth period
(ii) a Wherein the set event threshold value is 0.5;
step 2.5, for the presence of load edge events
Period m of (a), if
Then the load edge event is judged to be a load starting edge event
(ii) a On the contrary, if
Then the load edge event is determined to be a load shutdown edge event
；
Step 2.6, respectively calculating the active power increment of the load edge occurring at the mth period according to the following relational expression
Reactive power increment
Delta of DC current component
And a firstkIncrement of subharmonic current component
：
In the formula (I), the compound is shown in the specification,
active power for the x-th cycle,
Is the x-th period reactive power,
Is the x-th period direct current component,
is the x periodkThe sub-harmonic current effective value.
5. The method of non-intrusive load recognition battery power decomposition based on priority assignment as defined in claim 1,
in step 3, the eigenvector is a group of 9-dimensional eigenvectors, including an active power increment, a reactive power increment, an increment of a direct current component and an increment of a 2-7 harmonic current component of a load edge occurring at the mth cycle;
the electric appliance feature library comprises active power increment, reactive power increment, increment of direct current component and increment of 2-7 harmonic current component of each electric appliance.
6. The method of non-intrusive load recognition battery decomposition based on priority assignment as defined in claim 5,
the step 3 comprises the following steps:
step 3.1, traversing and calculating all Euclidean distances between the characteristic vectors and all the electric appliance characteristic vectors in the electric appliance characteristic library, and solving a maximum Euclidean distance value in the characteristic vectors;
step 3.2, if the obtained maximum Euclidean distance value is larger than a set distance threshold value, taking the type of the electric appliance in the electric appliance feature library corresponding to the maximum Euclidean distance value as the type of the load edge corresponding to the feature vector; wherein the set distance threshold value is 200.
7. The method of non-intrusive load recognition battery power decomposition based on priority assignment as defined in claim 1,
in step 4, the different types of the load edges are different types of the electric appliances in the electric appliance feature library, and the types of the electric appliances comprise electric appliances with stable working states and electric appliances with fluctuating working states;
the electric appliances with stable working states are classified into a first priority load, and the electric appliances with fluctuating working states are classified into a second priority load.
8. The method of non-intrusive load recognition battery power decomposition based on priority assignment as defined in claim 7,
the step 5 comprises the following steps:
step 5.1, for any load edge, shutting down the load to edge event
With a load start edge event occurring before the time of the load stop edge event
Matching to obtain load identification pairs
(ii) a Wherein the load stops the edge event
Take place inmAt cycle, load initiated edge events
Take place innAt period of time, satisfy
And the load edges corresponding to the two events are of the same kind;
step 5.2, identify the pair in any load
Next, the first priority load is decomposed to obtain the consumed power of the corresponding electrical appliance according to the following relation:
in the formula (I), the compound is shown in the specification,
step 5.3, identify the pair in any load
Next, integrating the total power of the second priority load from the starting time to the ending time of the second priority load to obtain the total electric quantity;
and 5.4, subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
9. The non-intrusive load identification electric quantity decomposition system based on priority distribution realized by using the non-intrusive load identification electric quantity decomposition method based on priority distribution of any one of claims 1 to 8,
the system comprises a high-frequency sampling module, a load edge detection module, a load edge identification module, a load priority distribution module and a load identification electric quantity decomposition module;
the high-frequency sampling module is used for carrying out high-frequency sampling on the current and the voltage of the household bus and carrying out fast Fourier transform on high-frequency sampling values of the current and the voltage so as to obtain active power, reactive power, direct current component and harmonic current component of a power frequency period;
the load edge detection module is used for detecting the load edge by utilizing the active power of the power frequency cycle and adopting a maximum likelihood ratio algorithm, and synchronously calculating the active power increment, the reactive power increment, the increment of the direct current component and the increment of the harmonic current component of the load edge;
the load edge identification module is used for extracting a characteristic vector from an active power increment, a reactive power increment, an increment of a direct current component and an increment of a harmonic current component of a load edge; identifying the type of the load edge by using the characteristic vector and each electric appliance characteristic vector in the electric appliance characteristic library through an Euclidean distance judgment method;
the load priority distribution module is used for carrying out multi-priority distribution on various loads according to different types of load edges so as to obtain a first priority load and a second priority load; the first priority load is a stability load, and the second priority load is a volatility load;
the load identification electric quantity decomposition module is used for matching a load shutdown edge with a load start edge for any load edge to form a load identification pair; and under any load identification pair, carrying out electric quantity decomposition on the first priority load to obtain the consumed electric quantity of the corresponding electric appliance, simultaneously obtaining the total electric quantity of the second priority load from starting to stopping, and subtracting the consumed electric quantity of the electric appliance corresponding to the first priority load from the total electric quantity to obtain the consumed electric quantity of the electric appliance corresponding to the second priority load.
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